System and method for air correction

ABSTRACT

A system and method for air calibration in a Computed Tomography (CT) imaging system are provided. A first set of data associated with air in a scanning area may be obtained. A second set of data associated with an object in the scanning area may be obtained. The second set of data based on the first set of data may be calibrated, and a set of reference values generated by a neural network model may be used to perform the calibration. A third set of data based on the calibration of the second set of data may be generated. Based on the third set of data, a CT image of the object may be generated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2017/092626 filed on Jul. 12, 2017, the entire contents of whichare hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image reconstruction, andmore specifically relates to methods and systems for medical imagereconstruction.

BACKGROUND

X-ray computed tomography (CT, Computed Tomography) is commonly used inmodern medicine technology. A computed tomography (CT) system mayinclude X-ray tubes and detector arrays that are rotated about a gantryencompassing a subject. The X-ray emitted through the X-ray tubes may beattenuated when going through the subject before it is received by theX-ray detector arrays. The X-ray detector arrays may transform thereceived X-rays to electrical signals, which may be used to perform animage reconstruction by the CT system.

Air correction may need to be performed on the electrical signalsgenerated by the X-ray detector arrays. In existing technology, areference detector by the side of the X-ray tube may be used to performthe air correction. However, to deploy a reference detector in the CTsystem may be cumbersome and the cost is expensive. The recentdevelopment of artificial intelligence (AI) may provide a solution inperforming the air correction instead of a reference detector.

SUMMARY

In accordance with some embodiments of the disclosed subject matter, asystem and methods for air calibration in a Computed Tomography (CT)imaging system are provided.

In accordance with some embodiments of the disclosed subject matter, amethod for air calibration may include one or more of the followingoperations. A first set of data associated with air in a scanning areamay be obtained. A second set of data associated with an object in thescanning area may be obtained. The second set of data based on the firstset of data may be calibrated, and a set of reference values generatedby a neural network model may be used to perform the calibration. Athird set of data based on the calibration of the second set of data maybe generated. Based on the third set of data, a CT image of the objectmay be generated.

In some embodiments, the neural network model may include a deeplearning neural network model.

In some embodiments, the neural network model may be trained using aplurality of training data associated with at least one detector of theCT imaging system.

In some embodiments, the plurality of training data may be obtained viathe at least one detector with respect to a plurality of scanningprotocols.

In some embodiments, the neural network model may include at least threelayers.

In some embodiments, the set of reference values generated by the neuralnetwork model may be view-dependent.

In some embodiments, the set of reference values generated by the neuralnetwork model may be slice-dependent.

In some embodiments, the calibrating the second set of data based on thefirst set of data using a neural network model further include one ormore following operations. A slice normalization on the first set ofdata and the second set of data may be performed. The second set of databased on the first set of data may be calibrated.

Another aspect of the present disclosure relates to a system for aircalibration. The system may include a computer-readable storage mediumand at least one processor. The computer-readable storage medium mayinclude a first set of instructions for calibrating data. The at leastone processor may communicate with the computer-readable storage medium,wherein when executing the first set of instructions, the at least oneprocessor is directed to perform one or more of the followingoperations. The at least one processor may obtain a first set of dataassociated with air in a scanning area. The at least one processor mayobtain a second set of data associated with an object in the scanningarea. The at least one processor may calibrate the second set of databased on the first set of data, and a set of reference values generatedby a neural network model is used to perform the calibration. The atleast one processor may generate a third set of data based on thecalibration of the second set of data. The at least one processor maygenerate a CT image of the object based on the third set of data.

Another aspect of the present disclosure relates to a non-transitorycomputer readable medium for air calibration. The non-transitorycomputer readable medium storing executable instructions that, whenexecuted by at least one processor, cause the at least one processor toeffectuate a method. The method may include one or more followingoperations. A first set of data associated with air in a scanning areamay be obtained. A second set of data associated with an object in thescanning area may be obtained. The second set of data based on the firstset of data may be calibrated, and a set of reference values generatedby a neural network model may be used to perform the calibration. Athird set of data based on the calibration of the second set of data maybe generated. Based on the third set of data, a CT image of the objectmay be generated.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1-A and FIG. 1-B are schematic diagrams illustrating an exemplaryCT system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary image generating processaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary correction engineaccording to some embodiments of the present disclosure;

FIG. 6 is a block diagram illustrating an exemplary correction moduleaccording to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for aircorrection according to some embodiments of the present disclosure.

FIG. 8 is a block diagram illustrating an exemplary neural networkaccording to some embodiments of the present disclosure.

FIG. 9 is a flowchart illustrating an exemplary process for acquiring areference values based on the neural network according to someembodiments of the present disclosure.

FIG. 10 is a flowchart illustrating an exemplary graphicalrepresentation of a neural network according to some embodiments of thepresent disclosure.

FIG. 11 is a flowchart illustrating an exemplary process of a slicenormalization correction according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnosis or research purposes. In some embodiments,the imaging system may be a computed tomography (CT) system, an emissioncomputed tomography (ECT) system, a magnetic resonance imaging (MM)system, an ultrasonography system, a positron emission tomography (PET)system, or the like, or any combination thereof.

The method and system disclosed herein may be used to perform anautomatic and self-improving correction when scanning an object. Thecorrection may be based on a neural network. In some embodiments, thecorrection may be based on a neural network of deep learning type. Forexample, the correction may be based on a deep believe network (DBN).Compared with existing technology, the method disclosure herein may bemore accurate and fast. The method disclosed herein is scalable inhandling large amount of CT raw data, while maintaining efficiency ofdata processing. The modeling within the neural network may bepersonalized or categorical. In other words, the modeling within theneural network may incorporate personal information relating to anobject, thus rendering the correction tailored to the personalcharacteristics. In some other embodiments, the modeling within theneural network may focus on the common features within a group ofobjects, thus rendering the correction tailored to the categoricalcharacteristic of the group. Further, the computation time may bereduced significantly after the neural network has been trained.

The method and system disclosure herein may be applied forreconstruction of other types of images including, for example, CTimages, ECT images, magnetic resonance (MR) images, PET images, etc. Forillustration purposes and not intended to limit the scope of the presentdisclosure, the disclosure is provided in connection with CT imagereconstruction. The system may reconstruct a CT image based on astatistical image reconstruction algorithm. The statistical imagereconstruction algorithm may include a regularization item that may beused to reduce staircase artifacts during the statistical imagereconstruction.

The following description is provided to help better understanding CTimage reconstruction methods and/or systems. The term “image” used inthis disclosure may refer to a 2D image, a 3D image, a 4D image, and/orany related image data (e.g., CT data, projection data corresponding tothe CT data). This is not intended to limit the scope the presentdisclosure. For persons having ordinary skills in the art, a certainamount of variations, changes, and/or modifications may be deductedunder the guidance of the present disclosure. Those variations, changes,and/or modifications do not depart from the scope of the presentdisclosure.

FIGS. 1-A and 1-B are schematic diagrams illustrating an exemplary CTsystem according to some embodiments of the present disclosure. As shownin FIG. 1-A, the CT system 100 may include a CT scanner 110, a network120, one or more terminals 130, a processing engine 140, and a storagedevice 150.

The CT scanner 110 may include a gantry 111, a detector 112, a detectingregion 113, a table 114, and a radioactive scanning source 115. Thegantry 111 may support the detector 112 and the radioactive scanningsource 115. A subject may be placed on the table 114 for scanning. Theradioactive scanning source 115 may emit radioactive rays to thesubject. The detector 112 may detect radiation events (e.g., gammaphotons) emitted from the detecting region 113. In some embodiments, thedetector 112 may include one or more detector units. The detector unitsmay include a scintillation detector (e.g., a cesium iodide detector), agas detector, etc. The detector unit may be and/or include a single-rowdetector and/or a multi-rows detector.

The network 120 may include any suitable network that can facilitateexchange of information and/or data for the CT system 100. In someembodiments, one or more components of the CT system 100 (e.g., the CTscanner 110, the terminal 130, the processing engine 140, the storagedevice 150, etc.) may communicate information and/or data with one ormore other components of the CT system 100 via the network 120. Forexample, the processing engine 140 may obtain image data from the CTscanner 110 via the network 120. As another example, the processingengine 140 may obtain user instructions from the terminal 130 via thenetwork 120. The network 120 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, witches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the CT system 100 may be connected to the network 120 toexchange data and/or information.

The terminal(s) 130 may include a mobile device 130-1, a tablet computer130-2, a laptop computer 130-3, or the like, or any combination thereof.In some embodiments, the mobile device 130-1 may include a smart homedevice, a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistance (PDA),a gaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing engine 140.

The processing engine 140 may process data and/or information obtainedfrom the CT scanner 110, the terminal 130, and/or the storage device150. For example, the processing engine 140 may process image data anddetermine a regularization item that may be used to modify the imagedata. In some embodiments, the processing engine 140 may be a singleserver or a server group. The server group may be centralized ordistributed. In some embodiments, the processing engine 140 may be localor remote. For example, the processing engine 140 may access informationand/or data stored in the CT scanner 110, the terminal 130, and/or thestorage device 150 via the network 120. As another example, theprocessing engine 140 may be directly connected to the CT scanner 110,the terminal 130 and/or the storage device 150 to access storedinformation and/or data. In some embodiments, the processing engine 140may be implemented on a cloud platform. Merely by way of example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or any combination thereof. In someembodiments, the processing engine 140 may be implemented by a computingdevice 200 having one or more components as illustrated in FIG. 2.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the terminal 130 and/or the processing engine 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the processing engine 140 may execute or use to perform exemplarymethods described in the present disclosure. In some embodiments, thestorage device 150 may include a mass storage, a removable storage, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage 150 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in the CTsystem 100 (e.g., the processing engine 140, the terminal 130, etc.).One or more components in the CT system 100 may access the data orinstructions stored in the storage device 150 via the network 120. Insome embodiments, the storage 150 may be directly connected to orcommunicate with one or more other components in the CT system 100(e.g., the processing engine 140, the terminal 130, etc.). In someembodiments, the storage device 150 may be part of the processing engine140. The connection between the components in the CT system 100 may bevariable. Merely by way of example, as illustrated in FIG. 1-A, the CTscanner 110 may be connected to the processing engine 140 through thenetwork 120. As another example, as illustrated in FIG. 1-B, the CTscanner 110 may be connected to the processing engine 140 directly.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure. As illustrated in FIG. 2, thecomputing device 200 may include a processor 210, a storage 220, aninput/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing engine 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the CT scanner 110, the terminal 130, the storage device150, and/or any other component of the CT system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both step A and step B, it should be understood that step A andstep B may also be performed by two or more different processors jointlyor separately in the computing device 200 (e.g., a first processorexecutes step A and a second processor executes step B, or the first andsecond processors jointly execute steps A and B).

The storage 220 may store data/information obtained from the CT scanner110, the terminal 130, the storage device 150, and/or any othercomponent of the CT system 100. In some embodiments, the storage 220 mayinclude a mass storage, a removable storage, a volatile read-and-writememory, a read-only memory (ROM), or the like, or any combinationthereof. For example, the mass storage may include a magnetic disk, anoptical disk, a solid-state drives, etc. The removable storage mayinclude a flash drive, a floppy disk, an optical disk, a memory card, azip disk, a magnetic tape, etc. The volatile read-and-write memory mayinclude a random access memory (RAM). The RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. The ROM may include a mask ROM (MROM), a programmable ROM (PROM),an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage 220 may storeone or more programs and/or instructions to perform exemplary methodsdescribed in the present disclosure. For example, the storage 220 maystore a program for the processing engine 140 for determining aregularization item.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing engine 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing engine 140 and theCT scanner 110, the terminal 130, and/or the storage 150. The connectionmay be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure. As illustrated in FIG. 3, themobile device 300 may include a communication platform 310, a display320, a graphic processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS™, Android™, Windows Phone™, etc.) and one or moreapplications 380 may be loaded into the memory 360 from the storage 390in order to be executed by the CPU 340. The applications 380 may includea browser or any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing engine 140. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing engine 140and/or other components of the CT system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a flowchart illustrating an exemplary image generating processaccording to some embodiments of the present disclosure. The flowchart400 may be implemented as a set of instructions in a non-transitorystorage medium of the processing engine 140 and/or the terminal 130 ofthe system 100. The processing engine 140 and/or the terminal 130 mayexecute the set of instructions and may accordingly perform the steps inthe flowchart 400.

In 401, the processing engine 140 and/or the terminal 130 may set one ormore parameters for a scan are. The parameters may be determined byscanning protocols. In some embodiments of the present disclosure, thescanning protocols may be generated for scanning different objects.Merely by way of example, the scanning protocols may be with respect toa collimator aperture, a detector aperture, an X-ray tube voltage and/orcurrent, a scan mode, a table index speed, a gantry speed, areconstruction field of view (FOV), kernel, or the like, or anycombination thereof. In some embodiments, the scan mode may furtherinclude a scanning time interval, a target location information, theposition of the gantry, or the like. By way of example, the table 114may be rotated to a location. As yet another example, the gantry 111 maybe moved to a location. In some embodiments, the locations may be set bya user (e.g., a doctor, a nurse). The positions may be differentdepending on the object to be scanned.

In step 403, raw data may be obtained by scanning an object based on theone or more parameters. Merely by way of example, the object may includea substance, a tissue, an organ, a specimen, a body, or the like, or anycombination thereof. In some embodiments, the object may include apatient or a part thereof. The objet may include a head, a breast, alung, a pleura, a mediastinum, an abdomen, a long intestine, a smallintestine, a bladder, a gallbladder, a triple warmer, a pelvic cavity, abackbone, extremities, a skeleton, a blood vessel, or the like, or anycombination thereof. The raw data may include the intensity of theX-rays. As an example, the raw data may be received by the detectorsthrough X-ray attenuation through object.

In 405, one or more images based on the raw data may be reconstructed.In some embodiments, 405 may be implemented by image processing engine140. The reconstruction images may include an MRI image, a CT image, aPET image, or any combination of the above-described images. In otherembodiments, the reconstruction images may include a two-dimensional(2D) image or a three-dimensional (3D) image. In some embodiments, thereconstruction process may include filtering denoising of the image, aircorrection, slice normalized correction, or the like. The reconstructionmay be performed using a plurality of algorithm. Merely by way ofexample, the reconstruction of the images may be based on methodsincluding Fourier slice theorem, filtered back projection algorithm,fan-beam reconstruction, iterative reconstruction, etc.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conduct under the teachingof the present disclosure. However, those variations and modificationsmay not depart from the protecting of the present disclosure.

FIG. 5 is a block diagram illustrating an exemplary correction engineaccording to some embodiments of the present disclosure. In someembodiments, the correction engine 500 may be implemented on theprocessing engine 140 and/or the terminal 130. The correction engine 500may include a data receiving module 510, a calibration module 520, animage reconstruction module 530, and a storage module 540.

The data receiving module 510 may be configured to acquire data relatedto a scan process (for example, a scan process in which an object isscanned) and/or data related to the imaging system. The data related toa scan process may include general information of the object, such asage, height and weight, gender, medical history, or the like, or anycombination thereof. In some embodiments, the data related to theimaging system may include scanning protocol, raw data, detectortemperature, correction parameters, X-ray intensity, or the like, or anycombination thereof. In some embodiment, the data receiving module 510may acquire data from the detector 112, such as the detectortemperature. In some embodiments, an operator (e.g., a doctor, a nurse)may set a temperature measurement device (not shown) at the terminal 130to acquire the detector temperature, and transmit the detectortemperature from the terminal 130 to the data receiving module 510. Insome embodiments, the radioactive scanning source 115 may emit theX-rays to the subject. The X-rays may pass through the subject and mayattenuate during the passing process. The attenuated X-rays may bedetected by the detector 112 and transmitted to the data receivingmodule 510. In some embodiments, the acquired data may be transmitted tothe storage module 540 to be stored. In some embodiments, the dataacquired by the data receiving module 510 may be transmitted to theimage reconstruction module 530 to construct the image, and may befurther transmitted the image calibration module 520 to correct thedata. For example, the data receiving module 510 may transmit thereceiving correction parameters to the image calibration module 520 forartifact correction and/or update a correction table.

The calibration module 520 may be configured to perform a correctionduring the process of image reconstruction. In some embodiments, thecorrection may include gas correction (air correction), centercorrection, water correction, slice normalization correction, or thelike, or any combination thereof. Air correction and water correctionmay produce a pre-scanned data in the air or water based on the scan(for example, a CT scan) of slices in the air or water. Thereafter, thepre-scanned data may be subtracted from the scanned data of the objectin a subsequent scan to obtain corrected scanned data of the object.Center correction or other types of system correction may also be usedto monitor the locations or the Cartesian coordinate of the X-ray tube.Slice normalization correction may be performed on the basis of the aircorrection. Merely by way of example, the air correction may not correctthe artifact caused by the difference between every slice of thedetectors. Thereafter, the data corrected by the slice normalizationcorrection together with the air correction data may be subtracted fromthe scanned data of the object in a subsequent scan. In someembodiments, the calibration module 520 may obtain raw data from thedata receiving module 510 and the storage module 540 and calibrate theraw data. In other embodiments, the calibration module 520 may performan air scanning. The air scanning is performed without object on thegantry 114 in the image system 100. For example, the calibration module520 may generate an air correction table by the air scanning. The aircorrection table may include one or more air correction parameters. Insome embodiments, the air correction table may be updated based on aplurality of reference values. The reference values may be generated bya neural network. The reference value may be the same data type as theone or more air correction parameters in the correction table.Additionally or alternatively, the air correction table may be stored inthe storage module 540. In some embodiments, the calibration module 520may transmit data to image reconstruction module 530 to reconstruct theimage.

The image reconstruction module 530 may be configured to reconstruct CTimages of a scanned object. In some embodiments, the imagereconstruction module 530 may reconstruct the images from the raw dataobtained from the data receiving module 510 and/or the corrected datafrom the calibration module 520. In some embodiments, the imagereconstruction module 530 may generate images according to the data fromthe storage module 540. In some embodiments, the image reconstructionmodule 530 may process the reconstructed images. The processing mayinclude smoothing, gray scale normalization, and the like, and anycombination thereof. For example, during an image reconstructionprocess, a surface of a tissue in an image may be smoothed. In someembodiments, the image reconstruction module 530 may reconstruct imagesaccording to reconstruction parameters. The reconstruction parametersmay include reconstruction field of view, reconstruction matrix,convolution kernel/reconstruction filter, or the like, or anycombination thereof. Merely by way of example, the reconstruction of theimages may be based on methods utilizing the Fourier slice theorem, thefiltered back projection algorithm, the fan-beam reconstruction, and/orthe iterative reconstruction, etc.

The storage module 540 may be configured or used to store informationreceived from the data receiving module 510, the calibration module 520,and/or the image reconstruction module 530. The information may includescanning protocols, scanning parameters, raw data, neural network, aircorrection table, air correction parameters, slice normalizationcorrection table, reconstructed images, reference values, or the like,or a combination thereof. In some embodiments, the storage module 540may store one or more programs and/or instructions that may be executedby processor(s) of the processing engine 140 to perform exemplarymethods described in this disclosure. For example, the storage module530 may store program(s) and/or instruction(s) that can be executed bythe processor(s) of the processing engine 140 to acquire raw data,reconstruct a CT image based on the raw data, and/or display anyintermediate result or a resultant image. In some embodiments, thestorage module 540 may include one or more components, including a harddisk driver, a magnetic tape, a removable storage drive (e.g., a phasechange rewritable optical disk drive, a magneto-optical drive, a USBremovable hard disk, etc.), a microdrive, or the like, or a combinationthereof.

It should be noted that the above description of the processing moduleis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, one or more units in the correction module 530may include a correction table generating unit (not shown) respectively.As another example, any two or more units may be combined as anindependent unit used to implement more than one functions. As a furtherexample, the function of the storage module 540 may be implemented onthe data receiving module 510 or the image reconstruction module 530, orthe combination thereof. As still a further example, any one of theunits may be divided into two or more sub-units.

FIG. 6 is a block diagram illustrating an exemplary correction moduleaccording to some embodiments of the present disclosure. The correctionmodule 520 may include a data acquisition 610, an air correction unit620, a slice normalization correction unit 630, and a neural networkunit 640, a correction table unit 650, a reference value generating unit660, and a correction data acquisition unit 670.

The data acquisition unit 610 may be configured to acquire data relatedto the imaging system. The data may include scanning data, scanningprotocol, image data, raw data, detector temperature, correctionparameters, X-ray intensity, or the like, or any combination thereof. Insome embodiments, the data acquisition 610 may acquire data from datareceiving module 510, the image reconstruction module 530, and/orstorage module 540. In some embodiments, the data acquisition 610 mayacquire data from any external device (e.g. database, terminals) relatedto the imaging system 100. In some embodiments, the data acquisitionunit 610 may acquire data from users (e.g. doctor, patient).

The air correction unit 620 may be configured to perform an aircorrection. The air correction may be performed based on different typesof data. The different types of data may include air scanning data, rawdata, scanning data, reference values, and air correction table. The airscanning data may be obtained by scanning air in a scanning area. Insome embodiments, the air scanning may be performed without any objecton the gantry 114 in the imaging system 100. In some embodiments, thereference values may include X-ray intensity values. Merely as anexample, the reference values may be generated in the referencedetectors of the imaging system 100 and used for performing an aircalibration in the reconstruction of an image. In some embodiments, thereference values may be generated by a neural network. In someembodiments, the air correction table may be obtained under differentscanning protocols. The air correction table may be affected by aplurality of factors. The factors may include inter-detector gain,system operation condition, season, ambient temperature. For instance,the air correction table may be changed if the temperature of thedetectors changed during working. In some embodiments, the aircorrection may require a period of time that lasts from a few minutes tomore than half-hour to be completed and is often performed only once aday before scanning a patient. The air correction unit 620 may performan air correction on the basis of data from the data acquisition unit610 and the neural network unit 640.

The slice normalization correction unit 630 may be configured to performa slice normalization correction for image reconstruction. In someembodiments, the slice normalization correction is a supplementcorrection for the air correction. As an example, the slicenormalization correction may be performed if the air correction couldnot distinguish the difference between every slice of the detectors. Thegain of every slice of the detectors may be different, and this maycause artifacts in the air correction. The artifacts may include ringartifacts, strip artifacts, etc. The slice normalization correction mayalso be performed based on the reference values generated by referencedetectors. In some embodiments, the reference detectors in the imagingsystem may include a detector array. The detector array has a pluralityof substantially contiguous rows of detectors and the fan beam of theimaging system is made sufficiently “thick” to illuminate all the rowsof detectors. The detector array may be configured to measure the flux,spatial distribution, spectrum, and/or other properties of X-rays, orthe like, or any combination thereof. In some embodiments, the referencedetectors located on both sides of the detectors. The reference valuesmay be generated by a neural network from the neural network unit 640.The slice normalization correction unit 620 may perform an aircorrection on the basis of data from the data acquisition unit 610, theair correction unit 630 and the neural network unit 640.

The neural network unit 640 may be configured to generate the referencevalues for the air correction and the slice normalization correction.The reference values may be associated with air scanning data, scanningdata, air correction table, slice normalization correction table,intensity of the X-ray, scanning protocol, temperature, or the like, orany combination thereof. In some embodiments, the neural network unit640 may include a plurality of neural networks. The neural network maybe trained with a general set of data to function as a general model ofa machine or process with an input set. The neural network may betested/trained with a set of training data The neural network may bedetermined according to different fields and problems. The neuralnetwork may include artificial neural network. In some embodiments, theartificial neural network may include neural network model such as Longshort term memory (LSTM) neural network, Deep believe network (DBN),Generative adversary network (GAN), Gradient boosting decision tree(GBDT), Back Propagation, Hopfield, Kohonen, Perceptron, Elmman, Jordan,or the like, or any combination thereof. The neural network unit 640 mayinclude a deep learning neural network model. In some embodiments, thelearning method may include unsupervised learning and supervisedlearning.

The correction table generating unit 650 may be configured to acquire acorrection table. The correction table may include an air correctiontable and/or a slice normalization correction table. The correctiontable may include one or more parameters. The one or more parameters mayinclude the intensity of the X-ray. The one or more parameters may begenerated by a reference detector (e.g. an edge detector). In someembodiments, the edge detector may be positioned on two sides ofdetectors. The intensity of the X-ray may be obtained at each angle bythe edge detector. In some embodiments, the correction table may begenerated based on the reference detector. Additionally oralternatively, the correction table may be stored in the storage module540. In some embodiments, the correction table generating unit 650 maytransmit data to the image reconstruction module 530 to reconstruct theimage. The reference value generating unit 660 may be configured toobtain the reference values for a correction. The correction may beperformed using the reference values generated from the neural networkunit 640. The correction data acquisition unit 670 may receive data fromthe correction table generating unit 650 and the reference valuegenerating unit 660.

It should be noted that the above description of the processing moduleis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the slice normalization correction unit 630 maybe integrated into the air correction unit 620. As another example, anytwo or more units may be combined as an independent unit used toimplement more than one functions. As a further example, the neuralnetwork unit 540 may be necessary and integrated into respect in boththe air correction unit 620 and the slice normalization correction unit630. As still a further example, any one of the units may be dividedinto two or more sub-units.

FIG. 7 is a flowchart illustrating an exemplary process for aircorrection according to some embodiments of the present disclosure. Insome embodiments, at least part of process 700 may be performed by or beimplemented on one or more components of the X-ray imaging system 100 asshown in FIG. 1.

In 701, an air correction table may be generated using a CT imagingsystem. In some embodiments, the air correction table may be generatedby the correction table generating unit 650. The air correction tablemay be generated under a scanning protocol. The scanning protocol mayinclude a plurality of parameters. Merely by way of example, theparameters may be with respect to a collimator aperture, a detectoraperture, an X-ray tube voltage and/or current, a scan mode, a tableindex speed, a gantry speed, a reconstruction field of view (FOV),kernel, or the like, or any combination thereof. In some embodiments,the air correction table is generated by performing an air scanning,i.e. no object in the gantry for scanning. During a scanning procedureof the patient, the detectors are exposed to one or more X-rays from theX-ray source and generate signals responsive to the one or more X-rayswhen only air is located between the X-ray source and the detectors. Insome embodiments, the air correction table is generated for eliminatingthe inter-detector gain inconsistency. The inter-detector gaininconsistency is affected by a plurality of factors of the detectors.The factors may include size of the pixel unit, surface flatness, thereflection between the pixel units, photoelectric conversion deviceresponse, and noise of the data acquisition of electronic system. Inother embodiments, the inter-detector gain may change with time andtemperature. The changes may include temperature changes, radiationdamage, changes in communication links that transmit data from thedetectors mounted on the gantry rotor to the gantry's stator. Theinter-detector gain inconsistency may produce ring artifacts duringimage reconstruction. In some embodiments, the air correction table isgenerate under a pre-set temperature and a pre-set scanning protocol.The air correction table may be different under different temperatureand different scanning protocol. The air correction table may be updatedregularly in a period of time or randomly. In some embodiments, the aircorrection table include air correction parameters, such as the returnvalue of the detectors, the intensity of the X-ray, etc.

In 703, a first set of data associated with air in a scanning area maybe obtained. In some embodiments, the first set of data may be obtainedby the air correction unit 620. The first set of data may be generatedin an air scanning. The air scanning is performed without object on thegantry. The first set of data may include the return value of thedetectors, the intensity of the X-ray, etc. In some embodiments, thefirst set of data may be related to the air correction table and/or theraw data.

In 705, a second set of data associated with an object in a scanningarea may be obtained. In some embodiments, the second set of data may beobtained by the air correction unit 620. The second set of data may begenerated in a CT scanning under the same scanning protocol as the airscanning. The CT scanning is performed with an object on the gantry. Inother embodiments, the object may include a substance, a tissue, anorgan, a specimen, a body, or the like, or any combination thereof. Insome embodiments, the object may include a patient or a part thereof.The object may include a head, a breast, a lung, a pleura, amediastinum, an abdomen, a long intestine, a small intestine, a bladder,a gallbladder, a triple warmer, a pelvic cavity, a backbone,extremities, a skeleton, a blood vessel, or the like, or any combinationthereof. The second set of data may include the return value of thedetectors across the object, the intensity of the X-ray across theobject.

In 707, the second set of data is calibrated based on the first set ofdata using a set of reference values generated by a neural network modeland the air correction table. The calibration may be performed by thereference value generating unit 660. In some embodiments, thecalibrating may be implemented by an algorithm as an example shownbelow:(μD)_(obj)=(A _(DTC) _(_) _(obj) −A _(ALT) _(_) _(obj))−(A _(DTC) _(_)_(air) −A _(ALT) _(_) _(air))  (1)A _(ALT)=ƒ(A _(DTC) _(_) _(obj) ,A _(DTC) _(_) _(air) ,mA, . . . )  (2)

wherein A_(DTC-obj) is the return value of the detectors across theobject, A_(DTC-air) is the air correction table, A_(ALT-air) isreference value that represents the return value of the detectors acrossthe air, A_(ALT-obj) is reference value that represents the return valueof the detectors across the object, mA is the milli-ampere value of theX-ray tube current, ƒ is a deep learning neural network where the outputof f, A_(ALT), is the estimated value to mimic the reference value givenby each row of detectors under each view.

In some embodiments, the calibrating may be implemented by an algorithmas an example:A _(ALT)(:,j)=ƒ(A _(DTC-obj)(:,j−p:j+q),A _(DTC-air) ,mA(j−p:j+q), . . .)  (3)

wherein j is the index indicating the j^(th) view of the detectors,A_(DTC-obj)(:,j−p:j+q) contains the raw data from the p view detectorsbefore the j^(th) view detectors to the q view detectors after thej^(th) view detectors across the object, A_(DTC-air) is the aircorrection table, mA(j−p:j+q) indicates the milli-ampere value of theX-ray tube current from the p view detectors before the j^(th) viewdetectors to the q view detectors after the j^(th) view detectors,A_(ALT)(:,j) is the alternate reference value that represents the returnvalue of the j^(th) view detectors, ƒ is a deep learning neural network.

In some embodiments, the neural network may include at least three typesof layers (e.g. one or more input layers, one or more hidden layers, andone or more output layers). The input layers may be configured to inputdata. The data may include the return value of the detectorsA_(DTC-obj), the air correction table A_(DTC-air), the intensity of theX-ray and/or other scanning data. The hidden layers may be configured toprocess the data from the input layers. The output layers may beconfigured to output the processed data from the hidden layers. Theoutput data may include reference values (e.g. A_(ALT-obj),A_(ALT-air)). The neural network may include a deep learning neuralnetwork model. The deep learning neural network model may be trainedusing a plurality of training data associated with at least one detectorof the CT imaging system. The plurality of training data includes a setof data covering the return values of desired reference detectors undereach of the scan protocol required. In some embodiments, the pluralityof training data may be obtained through experiments and/orcomputational simulation. Simulation of the return values of desiredreference detectors fluctuation may be added into the plurality oftraining data to form a new set of data. The new set of data may be anew training data for the neural network. The goal of the deep learningneural network is to minimize the value of the min∥{y_(i)}−{y_(i)}₀∥,wherein {y_(i)}₀ are the return values of desired reference detectorsand {y_(i)} are the output values of the neural network which representthe reference values.

In 709, a third set of data based on the correction of the second set ofdata is generated. In some embodiments, the third set of data may begenerated by the correction data acquisition unit 670. The first set ofdata associated with air in a scanning area may be subtracted from thesecond set of data associated with an object in the scanning area toobtain the third set of data (e.g. (μD)_(obj)).

In 711, a CT image of the object based on the third set of the data isgenerated. The object may be a human body (for example, a patient), apart of the human body, an X-ray-safe item whose inner structure neededto be imaged non-invasively or non-destructively (e.g., an antique, aninstrument, etc.), or the like. Merely by way of example, the second setof data may be generated by a CT scan or it may be obtained from otherresources (e.g., a computer-simulated scan). In some embodiments, the CTimage is generated according to a reconstruction algorithm may generatea CT image relating to the object obtained from a transformation of thethird set of data. Examples of such reconstruction algorithms mayinclude those based on Feldkamp-Davis-Kress (FDK) reconstruction,maximum a posteriori probability (MAP), maximum likelihood (ML),algebraic reconstruction technique (ART), entropy-based optimization,least squares (LS) or penalized weighted least squares (PWLS), or thelike, or a combination thereof. The described algorithms may be executedonce, or may be executed iteratively. Additionally, the third set ofdata acquired may proceed to noise estimation. A noise model may beacquired by noise estimation. The noise estimation may includeestimating the noise contained in the third set of data by fitting oneor more noise models to the estimated noise. As used herein, the noisemay include electronic noises that may be generated by an electronicdevice, e.g., a sensor, the circuitry of the scanner, or the like, or acombination thereof. The noise model(s) may indicate the noisedistribution of an image, noise amplitude at respective point(s) of animage, or the like, or a combination thereof. The generation of the CTimage described may include an iterative reconstruction process that mayinclude a computer based iterative processing The above mentionedexamples of models are provided for illustration purposes and notintended to limit the scope of the present disclosure. Exemplaryreconstruction parameters may be the slice thickness, parametersrelating to the voxel model (for example, a rectangular voxel model, acubic voxel model, a spherical voxel model, etc.), or the like, or acombination thereof. As used herein, reconstruction parameters may beset by the users based on different conditions. In some embodiments, thenoise variance may be reduced. The method for reducing the noisevariation may include obtaining the second set of data by scanning anobject, calibrating the second set of data by air calibration to obtainthe third set of data, generating a calibrated noise variance based onthe third set of data, and reducing the calibrated noise variance toproduce a reduced noise variance.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, step701 may be unnecessary and the third set of data may be generated basedon more data.

FIG. 8 is a block diagram illustrating an exemplary neural networkaccording to some embodiments of the present disclosure. The neuralnetwork may be implemented on the processing engine 140 and/or terminal130. The neural network may include a training unit 810, a testing unit820, and a determination unit 830. The training unit 810 may beconfigured to obtain a set of training data. The testing unit 820 may beconfigured to test the neural network trained by the training unit 810.The determination unit 830 may be configured to determine an appropriateneural network according to different technical fields and problems tobe solved.

FIG. 9 is a flowchart illustrating an exemplary process for acquiring areference values based on the neural network according to someembodiments of the present disclosure.

In 901, a plurality of training data associated with at least onedetector of the CT imaging system is acquired. The plurality of trainingdata may be acquired by the training unit 810. The plurality of trainingdata includes a set of data covering the return values of desiredreference detectors of each of the scanning protocol required. In someembodiments, the plurality of training data may be obtained throughexperiments and/or computational simulation. Simulation of the returnvalues of desired reference detectors fluctuation may be added into theplurality of training data to form a new set of data. The fluctuationmay include a parameter, a function, etc. In some embodiments, thetraining data may be pre-processed according to various algorithms. Thepre-processing may include noise reduction, dimensionality reduction,sample selection, or the like, or any combination thereof. Thealgorithms may include factor analysis, clustering analysis, etc.

In 903, a neural network is trained based on the training data and theplurality of features. The neural network is trained by the trainingunit 810 and the testing unit 820. The neural network based on thetraining data may work as a general model. The plurality of features maybe defined according to technical fields and/or problems to be solved.The neural network may have properties of nonlinearity, non-convexity,non-locality, non-stationary, adaptivity, fault tolerance, or the like,or the combination of thereof. In some embodiments, training the neuralnetwork may be related to a plurality of algorithm and parameters. Thealgorithm may include non-linear calculation, integral transformation,gradient calculation, iteration, etc. The parameters may include errorfunction, weighting coefficient, rate of convergence, etc.

In 905, the neural network may be determined based on the training dataand the plurality of features. The neural network may be determined bythe determination unit 830. In some embodiments, the neural network maybe determined when the number of iterations reaches a thresholdaccording to the algorithm. A plurality of testing data is required forverifying the neural network. The plurality of testing data is used forassessing the performance of the neural network. In some embodiments,the testing data includes an input and an expected output which isobtained from a plurality of experimental data. The actual output of theneural network may be compared with the expected output. The differencebetween the outputs may be in a range. The neural network may bedetermined when the error function associated with the expected outputand the actual output varies in a pre-determined range.

In 907, a set of reference values based on the neural network isacquired. The neutral network works as a general model of a process withan input. The set of reference values may be determined by determinationunit 830. The input may include the return values of desired referencedetectors, A_(DTC-obj), the air correction table A_(DTC-air), theintensity of the X-ray and/or other scanning data. The output of theneural network may be a set of reference values. The reference valuesmay be the same data type with the air correction table.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, 903and 905 may be put in one step.

FIG. 10 is a flowchart illustrating an exemplary graphicalrepresentation of a neural network according to some embodiments of thepresent disclosure. A neural network according to the present disclosureincludes a plurality of neurons connected therein. As shown in FIG. 10,according to their functions, the plurality of neurons of the neuralnetwork can be divided into three different types of groups (i.e.,layers). The first type of layer may be an input layer for receiving aset of data representing the input pattern, the second type of layer maybe an output layer for providing a set of data representing the outputpattern, and the third type of layer having an arbitrary number ofneurons may be a hidden layer, which converts the input pattern into anoutput pattern. Since the number of neurons in each layer can bearbitrarily determined, the input layer and the output layer may includeone or more units to represent the input pattern and the output patternof the problem to be solved, respectively. The neural networks have beenused to achieve the calculation of differentiated objects or eventclassification methods. The neural network is first trained by a knowndata representation related to object or event classification, and thenused to distinguish unknown objects or event categories. The neuralnetwork is then trained by a data set containing the general set ofdata. The trained network will function as a general neural networkmodel. In some embodiments, a local neural network model is achievedusing a special set of training data and functions partially dependentupon the general neural network model.

FIG. 11 is a flowchart illustrating an exemplary process of a slicenormalization correction according to some embodiments of the presentdisclosure.

In 1101, a slice normalization correction table is generated. The slicenormalization correction table may be generated by the correction tablegenerating unit 650. In some embodiments, the slice normalizationcorrection may be generated in a scanning protocol the same with the aircorrection. The scanning protocol may include a plurality of parameters.Merely by way of example, the parameters may be with respect to acollimator aperture, a detector aperture, an X-ray tube voltage and/orcurrent, a scan mode, a table index speed, a gantry speed, areconstruction field of view (FOV), kernel, or the like, or anycombination thereof. In some embodiments, the slice normalizationcorrection table is generated by performing an air scanning. In someembodiments, the slice normalization correction table is generated foreliminating the difference of every slice of the detectors. Thedifference of every slice of the detectors is affected by a plurality offactors of the detectors. The factors may include the position of thedetectors, the angle of the detector. The slice normalization correctiontable may be updated after a period of time. In some embodiments, theslice normalization correction table include the return value of theedge detectors, the intensity of the X-ray, etc.

In 1103, a slice normalization is performed on the first set of data andthe second set of data.

In 1105, the second set of data is calibrated based on the first set ofdata based on the slice normalization result and the slice normalizationcorrection table by the neural network. In some embodiments, thecalibrating may be implemented by an algorithm as an example:(μD)_(obj)=(A _(DTC) _(_) _(obj) −A _(ALT) _(_) _(obj))−(A _(DTC) _(_)_(air) −A _(ALT) _(_) _(air))  (4)A _(ALT)=ƒ(A _(DTC) _(_) _(obj) ,A _(DTC) _(_) _(air) ,mA, . . . )  (5)

wherein ADTC-obj is the return value of the detectors across the object,ADTC-air is the air correction table, A_(REF-obj) is the return value ofthe reference detectors across the object, A_(REF-air) is the returnvalue of the reference detectors across the air, A_(ALT-slice) is thereturn value of the edge detectors across the air, mA is the intensityof the X-ray across the air, ƒ is a deep learning neural network.

In some embodiments, the input layers of the neural network may beconfigured to input data. The data may include the return value of thedetectors A_(DTC-obj), the air correction table A_(REF-air), theintensity of the X-ray and/or other scanning data. The hidden layers maybe configured to process the data from the input layers. The outputlayers may be configured to output the processed data from the hiddenlayers. The output data may include reference values (e.g.A_(ALT-slice)). The neural network may include a deep learning neuralnetwork model. The deep learning neural network model may be trainedusing a plurality of training data associated with at least one detectorof the CT imaging system. The plurality of training data includes a setof data covering the return values of desired reference detectors andthe edge detectors under each of the scan protocol required. In someembodiments, the plurality of training data may be obtained throughexperiments and/or computational simulation. Simulation of the returnvalues of desired edge detectors fluctuation may be added into theplurality of training data to form a new set of data. The new set ofdata may be a new training data for the neural network. The goal of thedeep learning neural network is to minimize the value of themin∥{y_(i)}−{y_(i)}₀∥, wherein the {y_(i)}₀ are the return values ofdesired edge detectors.

In 1107, a third set of data based on the calibration of the second setof data is generated. The first set of data associated with air in ascanning area may be subtracted from the second set of data associatedwith an object in the scanning area to obtain the third set of data.

In 1109, a CT image of the object is generated based on the third set ofthe data. This step is the same with step 711 which may be a reference.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, 1101may be omitted and the slice normalization table may be input by users.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by the present disclosure,and are within the spirit and scope of the exemplary embodiments of thepresent disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more non-transitory computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter lie inless than all features of a single foregoing disclosed embodiment.

I claim:
 1. A method for air calibration in a Computed Tomography (CT)imaging system implemented on at least one computing device each ofwhich has at least one processor and storage, the method comprising:obtaining a first set of data associated with air in a scanning area;obtaining a second set of data associated with an object in the scanningarea; calibrating the second set of data based on the first set of data,by using a set of reference values generated by a neural network model;generating a third set of data based on the calibration of the secondset of data; and generating a CT image of the object based on the thirdset of data.
 2. The method of claim 1, wherein the neural network modelincludes a deep learning neural network model.
 3. The method of claim 1,wherein the neural network model is trained using a plurality oftraining data associated with at least one detector of the CT imagingsystem.
 4. The method of claim 3, wherein the plurality of training datais obtained via the at least one detector with respect to a plurality ofscanning protocols.
 5. The method of claim 1, wherein the neural networkmodel includes at least three layers.
 6. The method of claim 1, whereinthe set of reference values generated by the neural network model areview-dependent.
 7. The method of claim 1, wherein the set of referencevalues generated by the neural network model are slice-dependent.
 8. Themethod of claim 1, wherein calibrating the second set of data based onthe first set of data using a neural network model further comprises:performing slice normalization on the first set of data and the secondset of data; and calibrating the second set of data based on the firstset of data.
 9. A CT imaging system, comprising: a computer-readablestorage medium storing a first set of instructions for calibrating data;at least one processor in communication with the computer-readablestorage medium, wherein when executing the first set of instructions,the at least one processor is directed to: obtain a first set of dataassociated with air in a scanning area; obtain a second set of dataassociated with an object in the scanning area; calibrate the second setof data based on the first set of data, by using a set of referencevalues generated by a neural network model; generate a third set of databased on the calibration of the second set of data; and generate a CTimage of the object based on the third set of data.
 10. The system ofclaim 9, wherein the neural network model includes a deep learningneural network model.
 11. The system of claim 10, wherein the neuralnetwork model is trained using a plurality of training data associatedwith at least one detector of the CT imaging system.
 12. The system ofclaim 11, wherein the plurality of training data is obtained via the atleast one detector with respect to a plurality of scanning protocols.13. The system of claim 9, wherein the neural network model includes atleast three layers.
 14. The system of claim 9, wherein the set ofreference values generated by the neural network model areview-dependent.
 15. The system of claim 9, wherein the output of theneural network is slice-dependent.
 16. The system of claim 9, whereincalibrating the second set of data based on the first set of data usinga neural network model further comprises: performing slice normalizationon the first set of data and the second set of data; and calibrating thesecond set of data based on the first set of data.
 17. A non-transitorycomputer readable medium storing executable instructions that, whenexecuted by at least one processor, cause the at least one processor toeffectuate a method comprising: obtaining a first set of data associatedwith air in a scanning area; obtaining a second set of data associatedwith an object in the scanning area; calibrating the second set of databased on the first set of data using a set of reference values generatedby a neural network model; generating a third set of data based on thecalibration of the second set of data; and generating a CT image of theobject based on the third set of data.
 18. The non-transitory computerreadable medium of claim 17, wherein the executable instructions, whenexecuted by at least one processor, cause the at least one processor toeffectuate a method comprising: performing slice normalization on thefirst set of data and the second set of data; and calibrating the secondset of data based on the first set of data based on the slicenormalization result.
 19. The non-transitory computer readable medium ofclaim 17, wherein the set of reference values generated by the neuralnetwork model are view-dependent.
 20. The non-transitory computerreadable medium of claim 17, wherein the set of reference valuesgenerated by the neural network model are slice-dependent.